Abstract

   The ‘Jingbaili’ pear is a national geographical indication product of
   China, featuring an oblate shape and being rich in nutrients. But the
   quality of the ‘Jingbaili’ pear is unstable. Xenia can cause changes in
   the quality of pears, but the effect of xenia on the ‘Jingbaili’ pear
   is unknown, and its mechanism is still unclear. In order to clarify the
   effect of pollination on the fruit quality of the ’Jingbaili’ pear,
   this research pollinated ‘Jingbaili’ pear flowers with the pollen of
   ‘Yali’ (JY), ‘Suli’ (JS) and ‘Huangli’ (JH). The results indicated that
   the mass, transverse diameter and longitudinal diameter of the JY group
   were significantly higher than the JS group and JH group. On the other
   hand, the pears of the JY group and JS group obtained higher soluble
   sugar content. The aroma content of characteristic compounds was higher
   in the JY group than in the JS group and JH group. Multivariate
   analysis revealed significant differences in the nonvolatile
   metabolites among the JY group, JS group and JH group, potentially
   explaining the variations in the nutritional and flavor compounds of
   the pears. Furthermore, this research investigated metabolic changes in
   the pears during development and ripening under the three types of
   pollination. The results showed that amino acid metabolism differed
   among these pollination types during development. These differences may
   be the cause of the observed variations in the pears. This research
   clarified the effect of xenia on the nutritional components and flavor
   substances in the ‘Jingbaili’ pear and could provide data support for
   improving the quality of the ‘Jingbaili’ pear.

   Keywords: ‘Jingbaili’ pear, xenia effect, volatile compounds,
   metabolomics, fruit maturation, nutrient composition

1. Introduction

   The ‘Jingbaili’ pear (Pyrus ussuriensis Maxim. cv. ‘Jingbaili’, JBP) is
   a highly regarded variety within the Pyrus ussuriensis species, which
   is well-liked by consumers due to its abundant nutritional content and
   robust flavor [[44]1]. The JBP is native to Dongshan Village, Mentougou
   District, Beijing (GPS N 40.014°, E 116.135°), and is cultivated in
   Beijing, Hebei and Liaoning. In 2012, geographical indication product
   protection was implemented for the JBP. Pears (Pyrus spp.) are
   self-incompatible fruit trees, and fruit formation mainly relies on
   cross-pollination [[45]2]. Therefore, selecting superior pollinating
   parents is crucial for obtaining high-quality pears. Nevertheless, the
   incorrect choice of pollinating varieties has resulted in a notable
   decline in the flavor and quality of the pears [[46]3].

   The influence that pollen has on maternal tissues, including the seed
   coat and pericarp, is termed xenia [[47]3]. Xenia refers to the direct
   impact of pollen’s genetic composition on the diversity of seed and
   fruit development, as well as the physical characteristics that arise
   from fertilization until seed germination [[48]4]. Hence, research on
   the xenia effect is of great significance to the production of fruit
   trees. Multiple studies have demonstrated that xenia exerts a
   substantial influence on fruit quality. Several investigations have
   shown that the quality of pomelo fruits pollinated by Citrus
   mangshanensis differed significantly from that of fruits resulting from
   natural pollination, indicating a xenia effect [[49]5]. Similarly, the
   quality of ‘Yali’ pears was found to be significantly enhanced after
   pollination with ‘Hongnanguo’ and ‘Xuehuali’ pears [[50]3]. Although
   the xenia effect can improve pear quality, studies on its impact on the
   quality of the JBP have not been reported. Furthermore, the effect of
   different pollen varieties on the quality of the JBP is unknown, and
   the exact mechanisms by which volatile compounds and metabolites are
   associated with the xenia effect remain unclear.

   The quality and flavor of fruits are determined by metabolites such as
   sugars, organic acids, polyphenols and amino acids [[51]6]. The
   sugar-acid fractions present in fruits are the fundamental components
   that constitute the flavor of the fruit. The specific types and amounts
   of soluble sugars and organic acids have a direct impact on the sweet
   and sour taste of fruits [[52]7]. Polyphenols are crucial bioactive
   compounds and minerals found in fruits [[53]8]. The composition and
   abundance of these chemicals serve as significant indicators for
   assessing fruit quality and defining taste.

   Research has described that metabolite changes occurring in developing
   and ripening pears are similar to those reported previously, including
   changes in sugars, organic acids and some amino acids [[54]9].
   Furthermore, metabolomics was employed to investigate the impact of
   xenia effects on aroma quality in ‘Yali’ pears. After pollinating with
   ‘Hongnanguo’ pollen, the total aroma and ester content of ‘Yali’ pears
   were significantly higher than those of pears pollinated with
   ‘Xuehuali’ pollen [[55]3]. The process of metabolites formation during
   fruit ripening is intricate, and there is a dearth of research on
   metabolites associated with the JBP.

   In this research, ‘Yali’ (Pyrus bretschneideri Rehd. cv. ‘Yali’),
   ‘Suli’ (Pyrus bretschneideri Rehd. ‘Suli’) and ‘Huangli’ (Pyrus
   pyrifolia ‘Huangli’) were selected as parent varieties to investigate
   variations in the nutritional quality of the JBP at different
   developmental stages and to understand the process by which fragrance
   quality is formed after pollination. ‘Yali’ is often used for
   pollination due to its unique fragrance, delicate aroma, sweetness and
   crispness [[56]3]. ‘Suli’ is often used for pollination due to its
   strong aroma and abundant edible fruits [[57]10]. ‘Huangli’ is often
   used for pollination due to its status as an excellent local cultivar
   originally from the mountainous regions of Guizhou, China [[58]11]. The
   aim of this research was to analyze the varying impact of xenia on the
   volatile compounds of the JBP and the connection between the
   nonvolatile metabolites and the development of aroma quality. By
   meticulously examining the intricacies of pollination, this endeavor
   establishes a robust and coherent theoretical foundation. It
   facilitates the determination of optimal pollination varieties for the
   JBP. It also deepens our understanding of xenia’s multifaceted impacts,
   leading to more comprehensive and interconnected research in this
   field.

2. Materials and Methods

2.1. Chemical and Reagents

   The volatile compound standards for 33 aldehydes mixed standard in
   acetone (1000 μg/mL), 41 ketones mixed standard in acetone (1000 μg/mL)
   and 65 esters mixed standard in acetone (1000 μg/mL) were purchased
   from Alta Scientific (Tianjin, China). Formic acid and acetonitrile
   were purchased from Yuanye Biological Technology Co., Ltd. (Shanghai,
   China). Other chemical reagents were all domestically produced
   analytical pure and purchased from Aladdin (Shanghai, China).

2.2. Experimental Design

   The experiment was carried out from 2023 in Mengwu Ecological Park,
   Mentougou District, Beijing, China. Three distinct types (‘Yali’,
   ‘Suli’, ‘Huangli’) of pollen were chosen to pollinate the JBP in three
   separate orchards. The fruits of ‘Jingbaili’ pear × ‘Yali’ pear
   (pollinator), ‘Jingbaili’ pear × ‘Suli’ pear (pollinator) and
   ‘Jingbaili’ pear × ‘Huangli’ pear (pollinator) were named the JY group,
   JS group and JH group, respectively. Six trees that were approximately
   50 years old were selected for pollination in each orchard [[59]12].
   Each orchard was spaced around 10 m apart. Windless conditions were
   preferred to minimize any external disturbances. Additionally, the
   pollinated pears were covered with bags for a duration of 7 days after
   the pollination process [[60]3]. The same cultivation management
   measures were applied to all three orchards. JBPs were collected
   randomly from different directions of each tree at the same height,
   with 10–20 pears collected each time [[61]13]. In the bloom period
   after 50 d (24 May), sampling was conducted once every 30 days a total
   of four times, ending when the pears ripened on 4 September [[62]13].
   After picking, the pears were immediately transported to the
   laboratory. They were stored at 4 °C; then, a portion was taken for
   quality analysis. The remaining pears were ground into powder using
   liquid nitrogen and stored in a −80 °C refrigerator.

2.3. Measurement of Physical and Chemical Properties

   The weight of each individual pear was measured with an electronic
   balance, while the lengthwise and widthwise dimensions of the pear were
   measured with vernier calipers ([63]Figure S1). The phenolics
   extraction procedure involved measuring 0.20 g of a finely ground
   lyophilized sample. Then, 5 mL of an 80% methanol solution was added to
   the sample. The mixture was subjected to ultrasound-assisted extraction
   at room temperature for 30 min. Subsequently, the supernatant was
   separated using centrifugation (10,000 rpm, 10 min, 4 °C). This
   extraction process was repeated twice. Finally, the volume of the
   resulting solution was adjusted to 10 mL [[64]14]. The extracts were
   used to determine the total phenolic, total flavonoid content and
   antioxidant capacity.

   The Foline-Ciocalteu colorimetric method was used to determine the
   total phenolic content. Specifically, 0.2 mL of the extract was
   pipetted, and 5 mL of distilled water and 0.6 mL of 1 mol/L
   Foline-Ciocalteu reagent were added. The mixture was then mixed and
   allowed to stand for 5 min. After that, 1.0 mL of 15% Na[2]CO[3] was
   added, and the volume was brought up to 10 mL with distilled water. The
   absorbance was measured at 760 nm after allowing the mixture to stand
   for 60 min at room temperature in the dark. A standard curve was
   obtained using gallic acid as a reference. The results were expressed
   as mg gallic acid equivalents (GAE)/g dry weight (DW), and each sample
   was assayed in parallel three times [[65]15].

   To determine the total flavonoids with the Aluminum Nitrate
   colorimetric method, 0.6 mL of the extract and 0.5 mL of 5% NaNO[3]
   were pipetted into a 10 mL centrifuge tube. The mixture was then mixed
   and allowed to react at room temperature for 6 min. Subsequently, 0.5
   mL of Al (NO[3])[3] was added to each tube, which was shaken well and
   allowed to react at room temperature for another 6 min. Then, 4 mL of
   NaOH was added, the volume was adjusted with water, and the mixture was
   shaken well again and allowed to stand at room temperature for 15 min.
   The absorbance was measured at a wavelength of 500 nm. A standard curve
   was obtained using rutin as a reference. The results were expressed as
   mg rutin equivalents (RE)/g DW, and each sample was assayed in parallel
   three times [[66]15].

   The antioxidant capacity was determined using DPPH, ABTS and FRAP kits
   from Sangon Biotech (Shanghai, China). We performed the assay according
   to the instructions provided with the kit. The soluble sugar content
   was determined using the anthrone colorimetric method [[67]16], and the
   titratable acid content was determined through NaOH titration [[68]17].

2.4. Volatile Compound Analysis

2.4.1. Sample Preparation

   Volatile compounds were extracted by a 50/30 μm
   divinylbenzene/carboxen/polydimethylsiloxane solid-phase
   microextraction (SPME) fiber (Supelco, Bellefonte, PA, USA) based on
   the literature [[69]18]. Chopped ripe pears that had been lyophilized
   by liquid nitrogen grinding at room temperature were used for sample
   preparation. First, 1 g of the frozen sample and 6 mL of saturated NaCl
   solution (0.36 g/mL) were transferred into 20 mL SPME vials, and the
   vials were sealed immediately. The injection vial was stirred at 40 °C
   and equilibrated in solution and headspace after 20 min; then, the
   fibers were exposed to the headspace of the SPME vial for 20 min, and
   finally the fibers were withdrawn and inserted into the heated syringe
   port of the GC and resolved at 250 °C for 5 min in splitless mode.

2.4.2. Gas Chromatographic Mass Spectrometry (GC-MS) Conditions

   GC-MS analysis was performed by a gas chromatograph (TRACE 1600, Thermo
   Fisher Scientific, Waltham, MA, USA) coupled to a mass spectrometer
   (ISQ 7610, Thermo Fisher Scientific). Compound separation was performed
   on a TG-5MS (30 m × 0.25 mm × 0.25 μm, Thermo Scientific) column.
   Helium at a constant flow rate (1 mL/min) was used as the carrier gas.
   The GC was warmed up using the following procedure: the initial
   temperature was 30 °C and was held for 3 min; then, it was warmed up to
   100 °C at 3 °C/min and held for 1 min; then, it was warmed up to 180 °C
   at 5 °C/min and held for 1 min; and finally it was warmed up to 280 °C
   at 10 °C/min and held for 5 min [[70]19]. The temperature of the
   transfer line was 280 °C. The temperature of the ion source was set at
   280 °C, and the mass spectrum was recorded at 70 eV in EI ionization
   mode with a scanning range of 50–500 amu.

   The initial identification of compounds was done by comparing the mass
   spectra of the samples with the National Institute of Standards and
   Technology (NIST) database ([71]https://www.sisweb.com/, accessed on 20
   March 2024). Volatile compounds were determined with a match higher
   than 750. Mass spectrometry (MS) identification was confirmed by
   standards. The standards used in this research were diluted to a
   certain concentration with acetone, step by step (20 μg/mL, 10 μg/mL, 5
   μg/mL, 2 μg/mL, 1 μg/mL), and the injection method was the same as that
   of the samples. Quantitative analysis was performed by plotting
   standard curves with the standards, data acquisition with Full Scan and
   selected ion monitoring (SIM) method.

2.5. Nonvolatile Metabolite Analysis

2.5.1. Sample Preparation

   For the sample preparation, 0.3 g of the sample was placed into a 10 mL
   centrifuge tube, and 5 mL of the methanol-acetonitrile water mixture
   (2:2:1) was added. The sample was then subjected to ultrasonicate for
   60 min at 50 °C. Centrifugation was carried out at 10,000 rpm for 10
   min at 4 °C, and the resulting supernatant was extracted with a small
   column of solid-phase extraction (Waters Oasis HLB). The treatment
   method is shown in [72]Table S2. The eluent was nitrogen-blasted to
   dryness and then dissolved with 0.4 mL of methanol, after which it was
   passed through a 0.22 μm organic filtration membrane before liquid
   chromatography mass spectrometry (LC-MS) analysis [[73]20].
   Subsequently, Quality Control (QC) samples were created by combining
   100 μL of each sample in an injection vial.

2.5.2. LC-MS Conditions

   The extract of the JBP was analyzed using a Vanquish HPLC system
   equipped with Orbitrap Exploris (Thermo Fisher Scientific, USA). The
   column temperature was 40 °C, the injection volume was 5 μL, the flow
   rate was 0.4 mL/min, the mobile phase A was 0.1% formic acid aqueous
   solution and the mobile phase B was 0.1% formic acid acetonitrile
   solution. The gradient elution was performed using the following
   program: 0 min, 5% B; 0.5 min, 5% B; 2.5 min, 40% B; 5.5 min, 50% B;
   7.5 min, 100% B; 12.5 min, 100% B; 12.6 min, 5% B; and 17 min, 5% B
   [[74]21].

   The mass spectrum parameters are as follows: Full Scan ranges (m/z):
   75–1000; Resolution: 60,000; RF Lens (%): 70; Intensity Threshold: 2 ×
   10^4; Ion Source Type: H-ESI; Ion Transfer Tube Temp: 325 °C; Vaporizer
   Temp: 350 °C. The data were collected in the DDA mode.

   The Compound Discoverer 3.3 program automatically identified these
   distinct metabolites from the raw data. The reliability of these
   metabolites should be confirmed by manual inspection. The structure of
   metabolites was identified using MS data search through the ChemSpider
   database and MS/MS spectrum mapping through the mzCloud database. Mass
   spectrometry data from 12 batches ([75]Table S1) of the samples
   collected from UPLC-MS/MS were imported into the online analysis tool
   MetaboAnalyst 6.0 for multivariate statistical analysis.

2.6. Statistical Analysis

   All data were generated from six experiments and exported to Microsoft
   Excel to be presented as the mean ± standard deviation (SD). Principal
   Component Analysis (PCA) and Partial Least Squares Discriminant
   Analysis (PLS-DA) with principal component loading plots and
   Hierarchical Cluster Analysis (HCA) with cluster heat maps were
   performed using MetaboAnalyst 6.0 ([76]https://www.metaboanalyst.ca/,
   accessed on 25 July 2024). SPSS Statistics 17 (Armonk, New York, NY,
   USA) was used for significance analysis, and Graphpad Prism (version
   8.0) was used to plot pie charts and bar graphs.

3. Results and Discussion

3.1. Effects of Xenia on the Physicochemical Properties of JBP

   The growth and development patterns of the JBP from different
   pollinations are essentially identical. Growth is comparatively slow
   during pre-fruit development, then transitions from rapid growth during
   the middle stage to a slowdown in the late stage. This indicates the
   presence of a significant xenia effect. At maturity, substantial
   differences were observed in the weight of a single fruit and the
   transverse and longitudinal diameters between the JH group, JY group
   and JS group ([77]Figure 1A–C). Research conducted to identify
   appropriate pollinating varieties for the tropics found that pollen
   from parents with varying affinities could have a substantial impact on
   fruit weight, fruit longitudinal diameter and fruit diameter [[78]22].
   In research on the xenia effect in pomegranate, a substantial impact of
   pollen source on fruit weight, fruit longitudinal diameter and fruit
   diameter was observed [[79]23].

Figure 1.

   [80]Figure 1
   [81]Open in a new tab

   Effects of xenia on the physicochemical properties of JBP. (A) weight,
   (B) transverse diameter, (C) longitudinal diameter, (D) total phenol,
   (E) total flavonoid, (F) DPPH, (G) ABTS, (H) FRAP, (I) soluble sugar
   content, (J) titratable acid content. Bar graphs and error bars
   represent the mean and standard deviation. Different letters a, b and c
   indicate significant differences between samples (p < 0.05). JY:
   ‘Jingbaili’ pear × ‘Yali’ pear, JS: ‘Jingbaili’ pear × ‘Suli’ pear, JH:
   ‘Jingbaili’ pear × ‘Huangli’ pear. 50 d: 50 d after full bloom, 80 d:
   80 d after full bloom, 110 d: 110 d after full bloom, 145 d: 145 d
   after full bloom.

   Phenolics are the most important secondary metabolites in pears, with
   good scavenging ability for excess free radicals in the human body and
   good antioxidant activity. The levels of total phenol and total
   flavonoids in the JBP decreased gradually over time, reaching their
   peak at 50 days after blooming and their lowest point at maturity
   ([82]Figure 1D,E). The antioxidant capacity of also showed a decreasing
   trend. The fruit exhibited the highest antioxidant capacity 50 days
   after flowering and the lowest at the ripening stage ([83]Figure 1F–H).
   The xenia effect had an impact on the total phenolics, total flavonoids
   content and antioxidant capacity of the pears, but it was not
   significant. In research on the xenia effect in the Gala apple, it was
   found that different pollens had significant effects on fruit
   sugar-to-acid ratio, total phenolics and total flavonoids, while there
   was no significant effect on titratable acid [[84]24]. It was also
   found that xenia had a significant effect on the total phenolics and
   total flavonoids content of kiwifruit [[85]25].

   Sugar and acid are the main components of pear flavor, and their
   content directly affects the taste and quality of the fruit. The xenia
   effect significantly impacted the fruit’s soluble sugar levels, but had
   no significant influence on its titratable acid levels ([86]Figure
   1I,J). Pollination from different varieties had significant effects on
   the soluble sugar content and titratable acid content of plum fruits,
   showing an obvious xenia effect [[87]26]. It was found that the xenia
   effect improved the quality of blueberry fruits and had significant
   effects on soluble sugar content and titratable acid content [[88]27].

   Summarizing the findings, the JBP exhibited a clear xenia phenomenon in
   terms of single fruit weight, transverse and longitudinal diameters,
   and soluble sugar content. However, there was no apparent effect on
   total phenol, total flavonoid content, antioxidant capacity, or
   titratable acid content. Through a comprehensive analysis of the
   physicochemical properties of pears resulting from three different
   pollination combinations, it was found that pollination with the ‘Yali’
   pear and ‘Suli’ pear is more suitable for JBP trees.

3.2. Effect of Xenia on Volatile Components of JBP

   Flavor is an important indicator of fruit quality, and the flavor of
   pears directly affects their market acceptance and consumer
   satisfaction. The representative total ion chromatograms (TIC) of the
   JBP are presented in [89]Figure S2. A total of 51 volatile compounds
   were detected, which can be categorized into 18 aldehydes, 12 esters,
   13 alcohols, 4 ketones and 4 other compounds ([90]Table S4). At
   maturity, the JY group contained 35 volatile compounds, consisting of
   14 aldehydes, 6 esters, 9 alcohols, 3 ketones and 3 other compounds
   ([91]Figure 2A). The fruits of the three differently pollinated JBPs
   contained a total of 29 volatile chemicals, primarily E-2-hexenal,
   2-hexenal, (E, E)-2,4-hexadienal, 3-hexen-1-ol, 1-hexanol and hexyl
   acetate ([92]Figure S3).

Figure 2.

   [93]Figure 2
   [94]Open in a new tab

   Effects of xenia on volatile compounds of ripe JBP. (A) Quantity of
   volatile compounds. (B) Changes in the relative content of volatile
   compounds in the pollination of ‘Yali’ pears. (C) Changes in the
   relative content of volatile compounds in the pollination of ‘Suli’
   pears. (D) Changes in the relative content of volatile compounds in the
   pollination of ‘Huangli’ pears.

   Aldehydes exhibit significant diversity among all the volatile
   compounds, followed by alcohol species and esters. Ketones and other
   compounds have a smaller range of types compared to aldehydes.
   Furthermore, the scent attributes of the pears are intricately linked
   not only to the quantities but also to the concentrations of volatile
   compounds. The proportion of aldehydes was notably high among the
   volatile compounds, with percentages of 56.82% (JY), 60.38% (JS) and
   53.85% (JH), respectively ([95]Figure 2B–D). Alcohols, which are the
   second most common aroma volatile, also exhibited variation across the
   pollination pears. Esters constituted the third most significant aroma
   component. Among the total volatile components, the level of other
   unclassified components was relatively low.

   Aroma is an intricate blend of several volatile molecules, constituting
   merely a fraction of the mass of fresh fruit [[96]28], yet it plays a
   significant role in determining fruit flavor. The decline in fruit
   flavor quality can be attributed to the reduction of aroma volatiles
   [[97]7]. Prior research involved the identification of fragrance
   compounds in fully matured fruits of 202 pear varieties using
   HS-SPME-GC-MS. A total of 221 volatile components were discovered. The
   research revealed that aldehydes, esters and alcohols were the
   prevailing scent constituents, with aldehydes being the most prevalent
   molecules [[98]29]. The volatile compounds of Korla balsam pears from
   12 orchards were analyzed by HS-SPME-GC-MS, and a total of 100 volatile
   compounds were detected. Aldehydes were also found to be the most
   abundant compounds among the volatile compounds [[99]30], which was in
   agreement with the results of our research.

   PCA could not differentiate the volatile components in the JY, JS and
   JH groups, but there was a trend towards segregation, likely due to
   biological differences among the pear trees. The eigenvalues PC1 and
   PC2 were 22.5% and 20.9%, respectively ([100]Figure 3A), with PC3, PC4
   and PC5 accounting for 11.2%, 7.8% and 6.2%, respectively, summing to
   68.6%. Supervised PLS-DA reduced within-group variation and maximized
   between-group variation, allowing for better characterization of the
   variability across the samples and aiding in the search for
   differential metabolites [[101]31]. The results of the PLS-DA showed
   that there was a significant difference between the three differently
   pollinated JBPs ([102]Figure 3B). The R^2 and Q^2 of the five-fold
   cross-test results of the PLS-DA were 0.9330 and 0.7530, respectively
   ([103]Figure S4), indicating that the PLS-DA is reliable and is not
   overfitted.

Figure 3.

   [104]Figure 3
   [105]Open in a new tab

   Effects of xenia on multivariate analysis of volatile compounds in ripe
   JBP. (A) PCA; (B) PLS-DA; (C) VIP > 1 in PLS-DA; (D) Clustering heat
   map analysis of differential metabolites (VIP > 1). The higher and
   lower relative contents are presented in red and blue, respectively.
   (For interpre-tation of the references to color in this figure legend,